TY - GEN
T1 - A Data-Driven Workload Prediction Model for Cloud Computing Using Machine Learning
AU - Vajpayee, Abhay
AU - Tiwari, Pawan Kumar
AU - Prakash, Shiv
AU - Yang, Tiansheng
AU - Rathore, Raikumar Singh
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2025/1/17
Y1 - 2025/1/17
N2 - Cloud computing (CC) technology is widely pay-per-use to provide efficient services to the user. Therefore, it is frequently used in business management because it is cheaper, scalable, and flexible but has some limitations. However, workload prediction is a challenging and complex problem. To address this issue, a data-driven workload prediction model inspired by a decision tree-based model of Machine Learning has been proposed using the benchmark dataset Google Cluster Workload Traces 2019. It is a large benchmark dataset used to analyze or predict workload behavior, resource allocation, or system performance. The main aim is to optimize user satisfaction and profit for cloud service providers and minimize the scalability of resources. The proposed model has been assessed using different evaluation parameters like RMSE, MSE, MAE, R-squared, training time, model size, and prediction speed. The results of this study show that the proposed model performs effectively and outperforms the contemporary model.
AB - Cloud computing (CC) technology is widely pay-per-use to provide efficient services to the user. Therefore, it is frequently used in business management because it is cheaper, scalable, and flexible but has some limitations. However, workload prediction is a challenging and complex problem. To address this issue, a data-driven workload prediction model inspired by a decision tree-based model of Machine Learning has been proposed using the benchmark dataset Google Cluster Workload Traces 2019. It is a large benchmark dataset used to analyze or predict workload behavior, resource allocation, or system performance. The main aim is to optimize user satisfaction and profit for cloud service providers and minimize the scalability of resources. The proposed model has been assessed using different evaluation parameters like RMSE, MSE, MAE, R-squared, training time, model size, and prediction speed. The results of this study show that the proposed model performs effectively and outperforms the contemporary model.
KW - Cloud Computing
KW - Google Cluster Workload Traces 2019 Dataset
KW - Machine Learning (ML)
KW - Regression Analysis
UR - http://www.scopus.com/inward/record.url?scp=85217217728&partnerID=8YFLogxK
U2 - 10.1109/dasa63652.2024.10836480
DO - 10.1109/dasa63652.2024.10836480
M3 - Conference contribution
SN - 979-8-3503-6911-3
T3 - 2024 International Conference on Decision Aid Sciences and Applications (DASA)
SP - 1
EP - 6
BT - 2024 International Conference on Decision Aid Sciences and Applications (DASA)
PB - Institute of Electrical and Electronics Engineers (IEEE)
T2 - 2024 International Conference on Decision Aid Sciences and Applications (DASA)
Y2 - 11 December 2024 through 12 December 2024
ER -